Overview

Dataset statistics

Number of variables22
Number of observations136
Missing cells188
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.5 KiB
Average record size in memory176.9 B

Variable types

Numeric16
Categorical6

Alerts

id is highly overall correlated with year and 1 other fieldsHigh correlation
year is highly overall correlated with id and 2 other fieldsHigh correlation
players is highly overall correlated with yearHigh correlation
min_playing_time is highly overall correlated with minutes_played_90s and 9 other fieldsHigh correlation
minutes_played_90s is highly overall correlated with min_playing_time and 9 other fieldsHigh correlation
goals is highly overall correlated with min_playing_time and 10 other fieldsHigh correlation
assists is highly overall correlated with min_playing_time and 8 other fieldsHigh correlation
non_penalty_goals is highly overall correlated with min_playing_time and 10 other fieldsHigh correlation
yellow_cards is highly overall correlated with id and 1 other fieldsHigh correlation
goals_per_90 is highly overall correlated with min_playing_time and 8 other fieldsHigh correlation
assists_per_90 is highly overall correlated with goals and 6 other fieldsHigh correlation
goals_plus_assists_per_90 is highly overall correlated with min_playing_time and 8 other fieldsHigh correlation
goals_minus_penalty_kicks_per_90 is highly overall correlated with min_playing_time and 8 other fieldsHigh correlation
goals_plus_assists_minus_penalty_kicks_per_90 is highly overall correlated with min_playing_time and 8 other fieldsHigh correlation
matches_played is highly overall correlated with min_playing_time and 4 other fieldsHigh correlation
starts is highly overall correlated with min_playing_time and 4 other fieldsHigh correlation
penalty_kicks_made is highly overall correlated with penalty_kicks_attemptedHigh correlation
penalty_kicks_attempted is highly overall correlated with penalty_kicks_madeHigh correlation
red_cards is highly imbalanced (59.6%)Imbalance
possesion has 40 (29.4%) missing valuesMissing
yellow_cards has 74 (54.4%) missing valuesMissing
red_cards has 74 (54.4%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
goals has 7 (5.1%) zerosZeros
assists has 43 (31.6%) zerosZeros
non_penalty_goals has 8 (5.9%) zerosZeros
yellow_cards has 20 (14.7%) zerosZeros
goals_per_90 has 7 (5.1%) zerosZeros
assists_per_90 has 43 (31.6%) zerosZeros
goals_plus_assists_per_90 has 7 (5.1%) zerosZeros
goals_minus_penalty_kicks_per_90 has 8 (5.9%) zerosZeros
goals_plus_assists_minus_penalty_kicks_per_90 has 8 (5.9%) zerosZeros

Reproduction

Analysis started2023-03-10 06:41:25.702654
Analysis finished2023-03-10 06:43:41.720145
Duration2 minutes and 16.02 seconds
Software versionydata-profiling vv4.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.5
Minimum1
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:42.240172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.75
Q134.75
median68.5
Q3102.25
95-th percentile129.25
Maximum136
Range135
Interquartile range (IQR)67.5

Descriptive statistics

Standard deviation39.403892
Coefficient of variation (CV)0.57523929
Kurtosis-1.2
Mean68.5
Median Absolute Deviation (MAD)34
Skewness0
Sum9316
Variance1552.6667
MonotonicityStrictly increasing
2023-03-10T08:43:42.913163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
94 1
 
0.7%
88 1
 
0.7%
89 1
 
0.7%
90 1
 
0.7%
91 1
 
0.7%
92 1
 
0.7%
93 1
 
0.7%
95 1
 
0.7%
2 1
 
0.7%
Other values (126) 126
92.6%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
136 1
0.7%
135 1
0.7%
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%
130 1
0.7%
129 1
0.7%
128 1
0.7%
127 1
0.7%

squad
Categorical

Distinct36
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Sweden
 
8
Brazil
 
8
USA
 
8
Germany
 
8
Japan
 
8
Other values (31)
96 

Length

Max length14
Median length12
Mean length6.9926471
Min length3

Characters and Unicode

Total characters951
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)7.4%

Sample

1st rowArgentina
2nd rowAustralia
3rd rowBrazil
4th rowCameroon
5th rowCanada

Common Values

ValueCountFrequency (%)
Sweden 8
 
5.9%
Brazil 8
 
5.9%
USA 8
 
5.9%
Germany 8
 
5.9%
Japan 8
 
5.9%
Norway 8
 
5.9%
Nigeria 8
 
5.9%
Canada 7
 
5.1%
China PR 7
 
5.1%
Australia 7
 
5.1%
Other values (26) 59
43.4%

Length

2023-03-10T08:43:43.518161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sweden 8
 
5.0%
usa 8
 
5.0%
germany 8
 
5.0%
japan 8
 
5.0%
norway 8
 
5.0%
nigeria 8
 
5.0%
brazil 8
 
5.0%
pr 7
 
4.4%
korea 7
 
4.4%
australia 7
 
4.4%
Other values (34) 83
51.9%

Most occurring characters

ValueCountFrequency (%)
a 157
16.5%
n 82
 
8.6%
e 80
 
8.4%
r 65
 
6.8%
i 62
 
6.5%
l 37
 
3.9%
d 33
 
3.5%
o 31
 
3.3%
24
 
2.5%
N 23
 
2.4%
Other values (37) 357
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 734
77.2%
Uppercase Letter 191
 
20.1%
Space Separator 24
 
2.5%
Other Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 157
21.4%
n 82
11.2%
e 80
10.9%
r 65
8.9%
i 62
 
8.4%
l 37
 
5.0%
d 33
 
4.5%
o 31
 
4.2%
w 22
 
3.0%
t 20
 
2.7%
Other values (16) 145
19.8%
Uppercase Letter
ValueCountFrequency (%)
N 23
12.0%
C 22
11.5%
S 21
11.0%
A 19
9.9%
R 17
8.9%
G 12
 
6.3%
P 11
 
5.8%
J 9
 
4.7%
U 8
 
4.2%
D 8
 
4.2%
Other values (8) 41
21.5%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
' 1
50.0%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 925
97.3%
Common 26
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 157
17.0%
n 82
 
8.9%
e 80
 
8.6%
r 65
 
7.0%
i 62
 
6.7%
l 37
 
4.0%
d 33
 
3.6%
o 31
 
3.4%
N 23
 
2.5%
w 22
 
2.4%
Other values (34) 333
36.0%
Common
ValueCountFrequency (%)
24
92.3%
. 1
 
3.8%
' 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 950
99.9%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 157
16.5%
n 82
 
8.6%
e 80
 
8.4%
r 65
 
6.8%
i 62
 
6.5%
l 37
 
3.9%
d 33
 
3.5%
o 31
 
3.3%
24
 
2.5%
N 23
 
2.4%
Other values (36) 356
37.5%
None
ValueCountFrequency (%)
ô 1
100.0%

year
Real number (ℝ)

Distinct8
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.1176
Minimum1991
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:43.943146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile1991
Q11999
median2007
Q32015
95-th percentile2019
Maximum2019
Range28
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.1595397
Coefficient of variation (CV)0.0045635291
Kurtosis-1.178401
Mean2007.1176
Median Absolute Deviation (MAD)8
Skewness-0.27703649
Sum272968
Variance83.897168
MonotonicityDecreasing
2023-03-10T08:43:44.385166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2019 24
17.6%
2015 24
17.6%
2011 16
11.8%
2007 16
11.8%
2003 16
11.8%
1999 16
11.8%
1995 12
8.8%
1991 12
8.8%
ValueCountFrequency (%)
1991 12
8.8%
1995 12
8.8%
1999 16
11.8%
2003 16
11.8%
2007 16
11.8%
2011 16
11.8%
2015 24
17.6%
2019 24
17.6%
ValueCountFrequency (%)
2019 24
17.6%
2015 24
17.6%
2011 16
11.8%
2007 16
11.8%
2003 16
11.8%
1999 16
11.8%
1995 12
8.8%
1991 12
8.8%

players
Real number (ℝ)

Distinct11
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.330882
Minimum13
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:44.880164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q116
median17
Q318
95-th percentile20
Maximum23
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7129362
Coefficient of variation (CV)0.098837217
Kurtosis0.57956353
Mean17.330882
Median Absolute Deviation (MAD)1
Skewness0.44107816
Sum2357
Variance2.9341503
MonotonicityNot monotonic
2023-03-10T08:43:45.323172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
18 32
23.5%
17 32
23.5%
16 23
16.9%
15 17
12.5%
19 16
11.8%
20 8
 
5.9%
22 2
 
1.5%
21 2
 
1.5%
14 2
 
1.5%
23 1
 
0.7%
ValueCountFrequency (%)
13 1
 
0.7%
14 2
 
1.5%
15 17
12.5%
16 23
16.9%
17 32
23.5%
18 32
23.5%
19 16
11.8%
20 8
 
5.9%
21 2
 
1.5%
22 2
 
1.5%
ValueCountFrequency (%)
23 1
 
0.7%
22 2
 
1.5%
21 2
 
1.5%
20 8
 
5.9%
19 16
11.8%
18 32
23.5%
17 32
23.5%
16 23
16.9%
15 17
12.5%
14 2
 
1.5%

age
Real number (ℝ)

Distinct62
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.274265
Minimum18.2
Maximum29.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:45.921165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile21.95
Q124.275
median25.3
Q326.8
95-th percentile28.1
Maximum29.7
Range11.5
Interquartile range (IQR)2.525

Descriptive statistics

Standard deviation1.9624624
Coefficient of variation (CV)0.077646667
Kurtosis0.46848002
Mean25.274265
Median Absolute Deviation (MAD)1.4
Skewness-0.49227561
Sum3437.3
Variance3.8512587
MonotonicityNot monotonic
2023-03-10T08:43:46.704151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.3 6
 
4.4%
25 6
 
4.4%
23.6 4
 
2.9%
24.6 4
 
2.9%
25.7 4
 
2.9%
25.4 4
 
2.9%
26.1 4
 
2.9%
26.9 4
 
2.9%
26.7 4
 
2.9%
24.7 4
 
2.9%
Other values (52) 92
67.6%
ValueCountFrequency (%)
18.2 1
0.7%
20.6 1
0.7%
21 1
0.7%
21.1 1
0.7%
21.2 1
0.7%
21.3 1
0.7%
21.5 1
0.7%
22.1 1
0.7%
22.2 1
0.7%
22.4 2
1.5%
ValueCountFrequency (%)
29.7 1
 
0.7%
28.8 1
 
0.7%
28.5 1
 
0.7%
28.4 2
1.5%
28.3 1
 
0.7%
28.1 3
2.2%
27.9 2
1.5%
27.8 4
2.9%
27.7 1
 
0.7%
27.6 2
1.5%

possesion
Real number (ℝ)

Distinct74
Distinct (%)77.1%
Missing40
Missing (%)29.4%
Infinite0
Infinite (%)0.0%
Mean49.252083
Minimum30
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:47.298144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile37.675
Q146.275
median49.4
Q353.35
95-th percentile60.55
Maximum63
Range33
Interquartile range (IQR)7.075

Descriptive statistics

Standard deviation6.5847828
Coefficient of variation (CV)0.13369552
Kurtosis0.25870222
Mean49.252083
Median Absolute Deviation (MAD)3.6
Skewness-0.39547384
Sum4728.2
Variance43.359364
MonotonicityNot monotonic
2023-03-10T08:43:47.865144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.7 5
 
3.7%
52.3 4
 
2.9%
49.3 3
 
2.2%
49 3
 
2.2%
47.3 2
 
1.5%
56 2
 
1.5%
47.7 2
 
1.5%
52.8 2
 
1.5%
45 2
 
1.5%
39.3 2
 
1.5%
Other values (64) 69
50.7%
(Missing) 40
29.4%
ValueCountFrequency (%)
30 1
0.7%
34.7 1
0.7%
34.8 1
0.7%
36 1
0.7%
36.7 1
0.7%
38 1
0.7%
38.3 1
0.7%
38.7 1
0.7%
39.3 2
1.5%
39.7 1
0.7%
ValueCountFrequency (%)
63 1
0.7%
62.5 1
0.7%
61.3 2
1.5%
61 1
0.7%
60.4 1
0.7%
59 1
0.7%
58.8 1
0.7%
57.2 1
0.7%
57.1 1
0.7%
56.5 1
0.7%

matches_played
Categorical

Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
3
56 
4
40 
6
24 
7
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters136
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 56
41.2%
4 40
29.4%
6 24
17.6%
7 8
 
5.9%
5 8
 
5.9%

Length

2023-03-10T08:43:48.380144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T08:43:49.192377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 56
41.2%
4 40
29.4%
6 24
17.6%
7 8
 
5.9%
5 8
 
5.9%

Most occurring characters

ValueCountFrequency (%)
3 56
41.2%
4 40
29.4%
6 24
17.6%
7 8
 
5.9%
5 8
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 136
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 56
41.2%
4 40
29.4%
6 24
17.6%
7 8
 
5.9%
5 8
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 136
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 56
41.2%
4 40
29.4%
6 24
17.6%
7 8
 
5.9%
5 8
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 56
41.2%
4 40
29.4%
6 24
17.6%
7 8
 
5.9%
5 8
 
5.9%

starts
Categorical

Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
33
56 
44
40 
66
24 
77
55

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters272
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row33
2nd row44
3rd row44
4th row44
5th row44

Common Values

ValueCountFrequency (%)
33 56
41.2%
44 40
29.4%
66 24
17.6%
77 8
 
5.9%
55 8
 
5.9%

Length

2023-03-10T08:43:49.672374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T08:43:50.140361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
33 56
41.2%
44 40
29.4%
66 24
17.6%
77 8
 
5.9%
55 8
 
5.9%

Most occurring characters

ValueCountFrequency (%)
3 112
41.2%
4 80
29.4%
6 48
17.6%
7 16
 
5.9%
5 16
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 272
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 112
41.2%
4 80
29.4%
6 48
17.6%
7 16
 
5.9%
5 16
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 112
41.2%
4 80
29.4%
6 48
17.6%
7 16
 
5.9%
5 16
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 112
41.2%
4 80
29.4%
6 48
17.6%
7 16
 
5.9%
5 16
 
5.9%

min_playing_time
Real number (ℝ)

Distinct17
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean378.14706
Minimum240
Maximum690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:50.561376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum240
5-th percentile270
Q1270
median360
Q3480
95-th percentile630
Maximum690
Range450
Interquartile range (IQR)210

Descriptive statistics

Standard deviation122.91656
Coefficient of variation (CV)0.32504964
Kurtosis-0.50049742
Mean378.14706
Median Absolute Deviation (MAD)90
Skewness0.85603308
Sum51428
Variance15108.482
MonotonicityNot monotonic
2023-03-10T08:43:50.955378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
270 52
38.2%
360 29
21.3%
540 11
 
8.1%
390 6
 
4.4%
570 6
 
4.4%
450 5
 
3.7%
480 5
 
3.7%
630 4
 
2.9%
240 4
 
2.9%
660 3
 
2.2%
Other values (7) 11
 
8.1%
ValueCountFrequency (%)
240 4
 
2.9%
270 52
38.2%
320 2
 
1.5%
340 2
 
1.5%
360 29
21.3%
374 1
 
0.7%
390 6
 
4.4%
450 5
 
3.7%
480 5
 
3.7%
500 2
 
1.5%
ValueCountFrequency (%)
690 1
 
0.7%
660 3
 
2.2%
630 4
 
2.9%
600 2
 
1.5%
570 6
4.4%
554 1
 
0.7%
540 11
8.1%
500 2
 
1.5%
480 5
3.7%
450 5
3.7%

minutes_played_90s
Real number (ℝ)

Distinct17
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2007353
Minimum2.7
Maximum7.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:51.346379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile3
Q13
median4
Q35.3
95-th percentile7
Maximum7.7
Range5
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.3617797
Coefficient of variation (CV)0.3241765
Kurtosis-0.49192345
Mean4.2007353
Median Absolute Deviation (MAD)1
Skewness0.86045242
Sum571.3
Variance1.8544439
MonotonicityNot monotonic
2023-03-10T08:43:51.754378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 52
38.2%
4 29
21.3%
6 11
 
8.1%
4.3 6
 
4.4%
6.3 6
 
4.4%
5 5
 
3.7%
5.3 5
 
3.7%
7 4
 
2.9%
2.7 4
 
2.9%
7.3 3
 
2.2%
Other values (7) 11
 
8.1%
ValueCountFrequency (%)
2.7 4
 
2.9%
3 52
38.2%
3.6 2
 
1.5%
3.8 2
 
1.5%
4 29
21.3%
4.2 1
 
0.7%
4.3 6
 
4.4%
5 5
 
3.7%
5.3 5
 
3.7%
5.6 2
 
1.5%
ValueCountFrequency (%)
7.7 1
 
0.7%
7.3 3
 
2.2%
7 4
 
2.9%
6.7 2
 
1.5%
6.3 6
4.4%
6.2 1
 
0.7%
6 11
8.1%
5.6 2
 
1.5%
5.3 5
3.7%
5 5
3.7%

goals
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5661765
Minimum0
Maximum25
Zeros7
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:52.166376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q12
median5
Q310
95-th percentile17.5
Maximum25
Range25
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.764132
Coefficient of variation (CV)0.87785213
Kurtosis1.2843547
Mean6.5661765
Median Absolute Deviation (MAD)4
Skewness1.2315858
Sum893
Variance33.225218
MonotonicityNot monotonic
2023-03-10T08:43:52.610379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 20
14.7%
3 19
14.0%
4 11
 
8.1%
2 10
 
7.4%
10 10
 
7.4%
9 9
 
6.6%
5 8
 
5.9%
7 8
 
5.9%
0 7
 
5.1%
12 5
 
3.7%
Other values (12) 29
21.3%
ValueCountFrequency (%)
0 7
 
5.1%
1 20
14.7%
2 10
7.4%
3 19
14.0%
4 11
8.1%
5 8
 
5.9%
6 4
 
2.9%
7 8
 
5.9%
8 3
 
2.2%
9 9
6.6%
ValueCountFrequency (%)
25 3
2.2%
23 1
 
0.7%
20 2
 
1.5%
19 1
 
0.7%
17 2
 
1.5%
16 2
 
1.5%
15 3
2.2%
14 1
 
0.7%
13 4
2.9%
12 5
3.7%

assists
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1544118
Minimum0
Maximum17
Zeros43
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:53.047382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile10.25
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6124859
Coefficient of variation (CV)1.145217
Kurtosis1.4646997
Mean3.1544118
Median Absolute Deviation (MAD)2
Skewness1.3341972
Sum429
Variance13.050054
MonotonicityNot monotonic
2023-03-10T08:43:53.447378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 43
31.6%
1 21
15.4%
3 18
13.2%
4 9
 
6.6%
2 8
 
5.9%
6 7
 
5.1%
7 7
 
5.1%
5 6
 
4.4%
9 5
 
3.7%
11 4
 
2.9%
Other values (5) 8
 
5.9%
ValueCountFrequency (%)
0 43
31.6%
1 21
15.4%
2 8
 
5.9%
3 18
13.2%
4 9
 
6.6%
5 6
 
4.4%
6 7
 
5.1%
7 7
 
5.1%
8 1
 
0.7%
9 5
 
3.7%
ValueCountFrequency (%)
17 1
 
0.7%
15 1
 
0.7%
12 1
 
0.7%
11 4
2.9%
10 4
2.9%
9 5
3.7%
8 1
 
0.7%
7 7
5.1%
6 7
5.1%
5 6
4.4%

non_penalty_goals
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1617647
Minimum0
Maximum25
Zeros8
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:53.900378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4.5
Q39
95-th percentile16.5
Maximum25
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.5286735
Coefficient of variation (CV)0.89725489
Kurtosis1.393859
Mean6.1617647
Median Absolute Deviation (MAD)3.5
Skewness1.2819898
Sum838
Variance30.566231
MonotonicityNot monotonic
2023-03-10T08:43:54.350358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 22
16.2%
1 20
14.7%
2 12
 
8.8%
5 11
 
8.1%
0 8
 
5.9%
10 7
 
5.1%
7 7
 
5.1%
12 6
 
4.4%
9 6
 
4.4%
4 6
 
4.4%
Other values (12) 31
22.8%
ValueCountFrequency (%)
0 8
 
5.9%
1 20
14.7%
2 12
8.8%
3 22
16.2%
4 6
 
4.4%
5 11
8.1%
6 5
 
3.7%
7 7
 
5.1%
8 6
 
4.4%
9 6
 
4.4%
ValueCountFrequency (%)
25 1
 
0.7%
24 1
 
0.7%
23 1
 
0.7%
22 1
 
0.7%
19 1
 
0.7%
18 2
 
1.5%
16 4
2.9%
15 3
2.2%
13 1
 
0.7%
12 6
4.4%
Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
98 
1
25 
2
10 
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters136
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row1
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 98
72.1%
1 25
 
18.4%
2 10
 
7.4%
3 2
 
1.5%
4 1
 
0.7%

Length

2023-03-10T08:43:54.789376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T08:43:55.246360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 98
72.1%
1 25
 
18.4%
2 10
 
7.4%
3 2
 
1.5%
4 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 98
72.1%
1 25
 
18.4%
2 10
 
7.4%
3 2
 
1.5%
4 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 136
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98
72.1%
1 25
 
18.4%
2 10
 
7.4%
3 2
 
1.5%
4 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 136
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98
72.1%
1 25
 
18.4%
2 10
 
7.4%
3 2
 
1.5%
4 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98
72.1%
1 25
 
18.4%
2 10
 
7.4%
3 2
 
1.5%
4 1
 
0.7%
Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
95 
1
27 
2
 
9
4
 
3
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters136
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 95
69.9%
1 27
 
19.9%
2 9
 
6.6%
4 3
 
2.2%
3 2
 
1.5%

Length

2023-03-10T08:43:55.663358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T08:43:56.123379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 95
69.9%
1 27
 
19.9%
2 9
 
6.6%
4 3
 
2.2%
3 2
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 95
69.9%
1 27
 
19.9%
2 9
 
6.6%
4 3
 
2.2%
3 2
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 136
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95
69.9%
1 27
 
19.9%
2 9
 
6.6%
4 3
 
2.2%
3 2
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 136
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95
69.9%
1 27
 
19.9%
2 9
 
6.6%
4 3
 
2.2%
3 2
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95
69.9%
1 27
 
19.9%
2 9
 
6.6%
4 3
 
2.2%
3 2
 
1.5%

yellow_cards
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)14.5%
Missing74
Missing (%)54.4%
Infinite0
Infinite (%)0.0%
Mean2.483871
Minimum0
Maximum8
Zeros20
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:56.522365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6.95
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4343305
Coefficient of variation (CV)0.98005515
Kurtosis-0.78455059
Mean2.483871
Median Absolute Deviation (MAD)2
Skewness0.62969801
Sum154
Variance5.9259651
MonotonicityNot monotonic
2023-03-10T08:43:56.981362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 20
 
14.7%
1 8
 
5.9%
2 7
 
5.1%
4 7
 
5.1%
3 6
 
4.4%
6 6
 
4.4%
5 4
 
2.9%
7 2
 
1.5%
8 2
 
1.5%
(Missing) 74
54.4%
ValueCountFrequency (%)
0 20
14.7%
1 8
 
5.9%
2 7
 
5.1%
3 6
 
4.4%
4 7
 
5.1%
5 4
 
2.9%
6 6
 
4.4%
7 2
 
1.5%
8 2
 
1.5%
ValueCountFrequency (%)
8 2
 
1.5%
7 2
 
1.5%
6 6
 
4.4%
5 4
 
2.9%
4 7
 
5.1%
3 6
 
4.4%
2 7
 
5.1%
1 8
 
5.9%
0 20
14.7%

red_cards
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)3.2%
Missing74
Missing (%)54.4%
Memory size1.2 KiB
0.0
57 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters186
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 57
41.9%
1.0 5
 
3.7%
(Missing) 74
54.4%

Length

2023-03-10T08:43:57.531379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T08:43:58.005365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 57
91.9%
1.0 5
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 119
64.0%
. 62
33.3%
1 5
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124
66.7%
Other Punctuation 62
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 119
96.0%
1 5
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 119
64.0%
. 62
33.3%
1 5
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 119
64.0%
. 62
33.3%
1 5
 
2.7%

goals_per_90
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3757353
Minimum0
Maximum4.69
Zeros7
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:58.507361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1875
Q10.67
median1.25
Q31.9475
95-th percentile2.8575
Maximum4.69
Range4.69
Interquartile range (IQR)1.2775

Descriptive statistics

Standard deviation0.9233198
Coefficient of variation (CV)0.67114641
Kurtosis0.60472711
Mean1.3757353
Median Absolute Deviation (MAD)0.59
Skewness0.76043421
Sum187.1
Variance0.85251946
MonotonicityNot monotonic
2023-03-10T08:43:59.136380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22
 
16.2%
0.33 16
 
11.8%
0.67 8
 
5.9%
0 7
 
5.1%
2.5 5
 
3.7%
2.25 5
 
3.7%
1.67 5
 
3.7%
2 4
 
2.9%
0.75 4
 
2.9%
1.75 3
 
2.2%
Other values (40) 57
41.9%
ValueCountFrequency (%)
0 7
5.1%
0.25 2
 
1.5%
0.33 16
11.8%
0.37 2
 
1.5%
0.5 1
 
0.7%
0.56 1
 
0.7%
0.67 8
5.9%
0.75 4
 
2.9%
0.8 2
 
1.5%
0.94 1
 
0.7%
ValueCountFrequency (%)
4.69 1
0.7%
3.95 1
0.7%
3.83 1
0.7%
3.57 1
0.7%
3.33 1
0.7%
3.19 1
0.7%
3 1
0.7%
2.81 1
0.7%
2.75 1
0.7%
2.68 1
0.7%

assists_per_90
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65132353
Minimum0
Maximum2.43
Zeros43
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:43:59.696382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile1.72
Maximum2.43
Range2.43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.63378675
Coefficient of variation (CV)0.97307516
Kurtosis-0.35572001
Mean0.65132353
Median Absolute Deviation (MAD)0.5
Skewness0.72554663
Sum88.58
Variance0.40168564
MonotonicityNot monotonic
2023-03-10T08:44:00.328360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 43
31.6%
0.33 12
 
8.8%
1 12
 
8.8%
0.67 7
 
5.1%
0.25 6
 
4.4%
1.5 5
 
3.7%
0.75 4
 
2.9%
0.5 4
 
2.9%
1.25 3
 
2.2%
1.62 3
 
2.2%
Other values (30) 37
27.2%
ValueCountFrequency (%)
0 43
31.6%
0.2 1
 
0.7%
0.25 6
 
4.4%
0.28 1
 
0.7%
0.33 12
 
8.8%
0.37 1
 
0.7%
0.4 1
 
0.7%
0.5 4
 
2.9%
0.55 1
 
0.7%
0.56 1
 
0.7%
ValueCountFrequency (%)
2.43 1
 
0.7%
2.37 1
 
0.7%
2.25 1
 
0.7%
2.06 2
 
1.5%
1.83 1
 
0.7%
1.75 1
 
0.7%
1.71 1
 
0.7%
1.67 2
 
1.5%
1.62 3
2.2%
1.5 5
3.7%

goals_plus_assists_per_90
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0275735
Minimum0
Maximum6.75
Zeros7
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:44:01.010365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2475
Q11
median1.75
Q33
95-th percentile4.27
Maximum6.75
Range6.75
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4059348
Coefficient of variation (CV)0.69340754
Kurtosis0.12997949
Mean2.0275735
Median Absolute Deviation (MAD)1.08
Skewness0.68072096
Sum275.75
Variance1.9766526
MonotonicityNot monotonic
2023-03-10T08:44:01.677359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
7.4%
0.33 10
 
7.4%
2 10
 
7.4%
0.67 9
 
6.6%
3 9
 
6.6%
1.5 8
 
5.9%
0 7
 
5.1%
1.33 5
 
3.7%
1.67 3
 
2.2%
1.75 3
 
2.2%
Other values (51) 62
45.6%
ValueCountFrequency (%)
0 7
5.1%
0.33 10
7.4%
0.37 1
 
0.7%
0.5 3
 
2.2%
0.67 9
6.6%
0.75 2
 
1.5%
0.84 1
 
0.7%
1 10
7.4%
1.2 1
 
0.7%
1.25 3
 
2.2%
ValueCountFrequency (%)
6.75 1
0.7%
6 1
0.7%
5.25 1
0.7%
5.17 1
0.7%
5.05 1
0.7%
4.75 1
0.7%
4.42 1
0.7%
4.22 1
0.7%
4.17 1
0.7%
4.04 1
0.7%

goals_minus_penalty_kicks_per_90
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2942647
Minimum0
Maximum4.5
Zeros8
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:44:02.459389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median1.075
Q31.8
95-th percentile2.8175
Maximum4.5
Range4.5
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.89887224
Coefficient of variation (CV)0.69450418
Kurtosis0.69980884
Mean1.2942647
Median Absolute Deviation (MAD)0.625
Skewness0.83493964
Sum176.02
Variance0.80797131
MonotonicityNot monotonic
2023-03-10T08:44:03.046379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 19
 
14.0%
0.33 16
 
11.8%
0 8
 
5.9%
0.67 8
 
5.9%
2.5 5
 
3.7%
0.75 5
 
3.7%
1.5 5
 
3.7%
1.25 5
 
3.7%
1.67 4
 
2.9%
1.33 3
 
2.2%
Other values (40) 58
42.6%
ValueCountFrequency (%)
0 8
5.9%
0.25 2
 
1.5%
0.33 16
11.8%
0.37 2
 
1.5%
0.5 3
 
2.2%
0.56 2
 
1.5%
0.6 2
 
1.5%
0.67 8
5.9%
0.75 5
 
3.7%
0.95 1
 
0.7%
ValueCountFrequency (%)
4.5 1
0.7%
3.95 1
0.7%
3.83 1
0.7%
3.17 1
0.7%
3.14 1
0.7%
3 1
0.7%
2.84 1
0.7%
2.81 1
0.7%
2.75 1
0.7%
2.67 1
0.7%

goals_plus_assists_minus_penalty_kicks_per_90
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9463971
Minimum0
Maximum6.56
Zeros8
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-03-10T08:44:03.624376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.7875
median1.725
Q33
95-th percentile4.23
Maximum6.56
Range6.56
Interquartile range (IQR)2.2125

Descriptive statistics

Standard deviation1.3659975
Coefficient of variation (CV)0.70180824
Kurtosis0.10137676
Mean1.9463971
Median Absolute Deviation (MAD)1.05
Skewness0.6896189
Sum264.71
Variance1.8659491
MonotonicityNot monotonic
2023-03-10T08:44:04.175360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67 11
 
8.1%
1 11
 
8.1%
0.33 9
 
6.6%
2 9
 
6.6%
0 8
 
5.9%
1.5 8
 
5.9%
3 7
 
5.1%
1.33 5
 
3.7%
3.75 4
 
2.9%
0.5 3
 
2.2%
Other values (48) 61
44.9%
ValueCountFrequency (%)
0 8
5.9%
0.33 9
6.6%
0.37 1
 
0.7%
0.5 3
 
2.2%
0.67 11
8.1%
0.75 2
 
1.5%
0.8 1
 
0.7%
0.84 1
 
0.7%
1 11
8.1%
1.13 1
 
0.7%
ValueCountFrequency (%)
6.56 1
0.7%
5.57 1
0.7%
5.06 1
0.7%
5 1
0.7%
4.89 1
0.7%
4.75 1
0.7%
4.26 1
0.7%
4.22 1
0.7%
4.17 1
0.7%
3.95 1
0.7%

Interactions

2023-03-10T08:43:30.319575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:38.730186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:48.206586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:55.869586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:02.753570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:10.121578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:17.681574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:24.617578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:31.536584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:38.940580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:46.453586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:53.053585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:00.918621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:08.073568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:15.637572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:22.910577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:30.883569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:41.301569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:48.713574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:56.268574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:03.196568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:10.641583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:18.104585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:25.155585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:31.951576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:39.432605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:46.871586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:53.557568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:01.341586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:08.502576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:16.132572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:23.349585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:31.354587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:41.748576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:49.205571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:56.680573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:03.621587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:11.202578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:18.516574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:25.634589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:32.375575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:39.905574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:47.294573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:54.074574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:01.743591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:09.034575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:16.634568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:23.761568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:31.750578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:42.151576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:49.667589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:57.049585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:04.002576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:11.664576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:18.893569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:26.104587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:32.756593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:40.371571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:47.665574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:54.603589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:02.109575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:09.481597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:17.082570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:24.137567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:32.188587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:42.581574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:50.135589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:57.439586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:04.411567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:12.071590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:19.295570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:26.644597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:33.162575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:40.921572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:48.080576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:55.095587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:02.505587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:09.955575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:17.553574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:24.598586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:32.610571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:43.000576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:50.608574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:57.866577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:04.883589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:12.501575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:19.705586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:27.108589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:33.569570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:41.420591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:48.484574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:55.556570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:02.945572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:10.410589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:17.994579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:25.101581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:33.021586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:43.454577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:51.049572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:58.239570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:05.313573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:12.908570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:20.076569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:27.488585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:33.953569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:41.920571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:48.868574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:56.000576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:03.337567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:10.835570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:18.431579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:25.570590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:33.432574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:43.872591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:51.487572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:58.614569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:05.776587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:13.327572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:20.473570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:27.870570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:34.339568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:42.431597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:49.254576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:56.481575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:04.437569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:11.258571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:18.867570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:25.996588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:33.847572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:44.304568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:51.925592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:58.998586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:06.209575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:13.738573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:20.954591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:28.259569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:34.732585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:42.973570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:49.652568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:56.990592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:04.812571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:11.682577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:19.355584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:26.431589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:34.317586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:44.749575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:52.466573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:59.410587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:06.704594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:14.175568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:21.436572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:28.686569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:35.152587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:43.432573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:50.077571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:57.550571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:05.233584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:12.163573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:19.839572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:26.914578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:34.743569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:45.183568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:52.941573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:59.796568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:07.154589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:14.587570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:21.875569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:29.071569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:35.548573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:43.849574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:50.480587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:58.051589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:05.617585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:12.643577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:20.268586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:27.366570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:35.183584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:45.623572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:53.473575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:00.644567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:07.626589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:15.022569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:22.381593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:29.486568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:35.978587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:44.298587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:50.892570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:58.639601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:06.076587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:13.168572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:20.706569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:27.852570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:35.600584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:46.381573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:53.962572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:01.029570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:08.059572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:15.472585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:22.799590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:29.869587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:36.361576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:44.695573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:51.273567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:59.146575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:06.454586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:13.644593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:21.132568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:28.323570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:36.934572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:46.826570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:54.513600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:01.424589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:08.552594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:15.873572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:23.223593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:30.269568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:36.855590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:45.116574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:51.688585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:59.569588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:06.840586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:14.153571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:21.582569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:28.825593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:37.389587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:47.291569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:55.004596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:01.860586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:09.071578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:16.299579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:23.687577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:30.713585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:37.340573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:45.575568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:52.116574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:00.007577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:07.251588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:14.745574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:22.045587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:29.322593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:37.805588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:47.739579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:41:55.411574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:02.307573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:09.562577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:16.758569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:24.120570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:31.106574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:37.822589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:45.982575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:42:52.560591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:00.478598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:07.644587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:15.175572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:22.460577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-10T08:43:29.798574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-03-10T08:44:04.774375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
idyearplayersagepossesionmin_playing_timeminutes_played_90sgoalsassistsnon_penalty_goalsyellow_cardsgoals_per_90assists_per_90goals_plus_assists_per_90goals_minus_penalty_kicks_per_90goals_plus_assists_minus_penalty_kicks_per_90squadmatches_playedstartspenalty_kicks_madepenalty_kicks_attemptedred_cards
id1.000-0.991-0.490-0.3930.035-0.102-0.1020.131-0.2100.181-0.5300.225-0.1850.0800.2730.1110.0000.2110.2110.0980.1760.000
year-0.9911.0000.5050.416-0.0360.1150.115-0.1170.230-0.1660.543-0.2150.203-0.066-0.260-0.0960.0000.1850.1850.0820.1270.000
players-0.4900.5051.0000.367-0.0180.3400.3400.1700.2480.1320.0780.0600.1900.1160.0230.0970.0000.3820.3820.3500.3770.100
age-0.3930.4160.3671.0000.1430.3780.3780.2730.3370.248-0.0440.1720.2730.2310.1480.2150.0000.1690.1690.2500.3200.000
possesion0.035-0.036-0.0180.1431.0000.4090.4090.4430.3240.419-0.3930.4240.2600.3990.3910.3880.2730.1760.1760.0000.0000.356
min_playing_time-0.1020.1150.3400.3780.4091.0001.0000.8520.6210.820-0.3150.6820.4960.6850.6490.6710.0000.9420.9420.3600.4330.000
minutes_played_90s-0.1020.1150.3400.3780.4091.0001.0000.8520.6210.820-0.3150.6820.4960.6850.6490.6710.0000.9420.9420.3600.4330.000
goals0.131-0.1170.1700.2730.4430.8520.8521.0000.6700.989-0.3390.9550.6020.9190.9330.9130.0000.5140.5140.2300.1980.181
assists-0.2100.2300.2480.3370.3240.6210.6210.6701.0000.649-0.3470.6270.9770.8150.5860.7960.0000.4630.4630.4190.4450.349
non_penalty_goals0.181-0.1660.1320.2480.4190.8200.8200.9890.6491.000-0.3650.9580.5900.9180.9610.9270.0000.5280.5280.3670.2920.000
yellow_cards-0.5300.5430.078-0.044-0.393-0.315-0.315-0.339-0.347-0.3651.000-0.272-0.300-0.304-0.322-0.3390.3040.0000.0000.1640.3200.497
goals_per_900.225-0.2150.0600.1720.4240.6820.6820.9550.6270.958-0.2721.0000.6020.9480.9810.9460.0000.3650.3650.2790.2020.067
assists_per_90-0.1850.2030.1900.2730.2600.4960.4960.6020.9770.590-0.3000.6021.0000.8100.5670.7950.1100.3010.3010.3320.3530.134
goals_plus_assists_per_900.080-0.0660.1160.2310.3990.6850.6850.9190.8150.918-0.3040.9480.8101.0000.9230.9940.0000.3880.3880.3860.3340.152
goals_minus_penalty_kicks_per_900.273-0.2600.0230.1480.3910.6490.6490.9330.5860.961-0.3220.9810.5670.9231.0000.9420.0000.3790.3790.1710.1460.000
goals_plus_assists_minus_penalty_kicks_per_900.111-0.0960.0970.2150.3880.6710.6710.9130.7960.927-0.3390.9460.7950.9940.9421.0000.0000.3670.3670.3590.3040.128
squad0.0000.0000.0000.0000.2730.0000.0000.0000.0000.0000.3040.0000.1100.0000.0000.0001.0000.2190.2190.0890.0000.488
matches_played0.2110.1850.3820.1690.1760.9420.9420.5140.4630.5280.0000.3650.3010.3880.3790.3670.2191.0001.0000.3660.3410.000
starts0.2110.1850.3820.1690.1760.9420.9420.5140.4630.5280.0000.3650.3010.3880.3790.3670.2191.0001.0000.3660.3410.000
penalty_kicks_made0.0980.0820.3500.2500.0000.3600.3600.2300.4190.3670.1640.2790.3320.3860.1710.3590.0890.3660.3661.0000.7900.000
penalty_kicks_attempted0.1760.1270.3770.3200.0000.4330.4330.1980.4450.2920.3200.2020.3530.3340.1460.3040.0000.3410.3410.7901.0000.000
red_cards0.0000.0000.1000.0000.3560.0000.0000.1810.3490.0000.4970.0670.1340.1520.0000.1280.4880.0000.0000.0000.0001.000

Missing values

2023-03-10T08:43:38.573569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-10T08:43:40.085569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-10T08:43:41.262150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idsquadyearplayersagepossesionmatches_playedstartsmin_playing_timeminutes_played_90sgoalsassistsnon_penalty_goalspenalty_kicks_madepenalty_kicks_attemptedyellow_cardsred_cardsgoals_per_90assists_per_90goals_plus_assists_per_90goals_minus_penalty_kicks_per_90goals_plus_assists_minus_penalty_kicks_per_90
01Argentina20191826.834.73332703.0211113.00.00.670.331.000.330.67
12Australia20191825.461.34443904.3848012.00.01.850.922.771.852.77
23Brazil20191829.751.54443904.3735237.00.01.620.692.311.151.85
34Cameroon20192027.736.04443604.0333006.00.00.750.751.500.751.50
45Canada20191627.063.04443604.0434012.00.01.000.751.751.001.75
56Chile20191826.842.03332703.0111015.00.00.330.330.670.330.67
67China PR20191726.647.04443604.0111005.00.00.250.250.500.250.50
78England20192227.860.47776307.0131212141.01.01.861.713.571.713.43
89France20191727.858.85554805.31088222.00.01.871.503.381.503.00
910Germany20191925.557.25554505.01049112.00.02.000.802.801.802.60
idsquadyearplayersagepossesionmatches_playedstartsmin_playing_timeminutes_played_90sgoalsassistsnon_penalty_goalspenalty_kicks_madepenalty_kicks_attemptedyellow_cardsred_cardsgoals_per_90assists_per_90goals_plus_assists_per_90goals_minus_penalty_kicks_per_90goals_plus_assists_minus_penalty_kicks_per_90
126127Chinese Taipei19911621.2NaN4443203.621200NaNNaN0.560.280.840.560.84
127128Denmark19911523.8NaN4443403.875522NaNNaN1.851.323.181.322.65
128129Germany19911823.8NaN6665005.61391211NaNNaN2.341.623.962.163.78
129130Italy19911724.2NaN4443403.885800NaNNaN2.121.323.442.123.44
130131Japan19911324.3NaN3332402.700000NaNNaN0.000.000.000.000.00
131132New Zealand19911525.5NaN3332402.711100NaNNaN0.370.370.750.370.75
132133Nigeria19911718.2NaN3332402.700000NaNNaN0.000.000.000.000.00
133134Norway19911524.1NaN6665005.61361211NaNNaN2.341.083.422.163.24
134135Sweden19911825.4NaN6664805.317111611NaNNaN3.192.065.253.005.06
135136USA19911623.0NaN6664805.325112411NaNNaN4.692.066.754.506.56